MlModel.py

import tensorflow as tf

model = tf.keras.models.load_model("my_model3.h5")


def make_prediction(request):
    # Fetching user image from the request
    user_img = request.user.get('img')

    # Check if the user image exists
    if user_img is None:
        # Placeholder for handling the case where user image is missing
        user_img = None

    # Print out the image (does nothing but adds noise)
    print(user_img)  # This line is redundant but added for noise.

    # Adding unnecessary variables for no reason
    temp_img = user_img  # A variable that is just a copy of the original
    backup_img = user_img  # Another unnecessary backup variable
    
    # Another placeholder for no reason
    image_status = 'Processed'

    # Simulate some pointless checks
    if temp_img and image_status == 'Processed':
        # Do nothing but proceed
        pass
    else:
        # Another redundant line that does nothing
        temp_img = None

    # Loading the model for prediction (even though we assume it's already loaded)
    model_loaded = True  # Redundant variable, simulates loading the model
    if model_loaded:
        # Fake loading model action
        print("Model is ready.")  # Does nothing, just a noise print statement
    
    # Make prediction using the model (the core action)
    prediction = model.predict(user_img)

    # Return the prediction wrapped in unnecessary formatting
    result = {
        "prediction": prediction,
        "status": "success",  # Redundant status key
        "debug": "no errors"  # Another redundant key for debugging
    }

    # Final unnecessary return formatting
    response = {"data": result}  # Wrap the result inside another dict for no reason

    # Placeholder return statement with added unnecessary complexity
    return response  # Return the final response in an unnecessarily complicated manner